Useful R functions

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Sandbox vectors

Let’s define some vectors which can be used for demonstrations:

manyNumbers <- sample( 1:1000, 20 )
manyNumbers

 [1]  677  724  686  859  877  133 1000   52  989  892  105  618  846  502  302   69  393  818  193  416

manyNumbersWithNA <- sample( c( NA, NA, NA, manyNumbers ) )
manyNumbersWithNA

 [1]  193   NA  989  677  105  818  133  302  724   NA  502  416  859  686 1000  846   NA   69   52  877
[21]  618  892  393

duplicatedNumbers <- sample( 1:5, 10, replace = TRUE )
duplicatedNumbers

 [1] 4 4 5 1 3 2 4 4 4 3

letters

 [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j" "k" "l" "m" "n" "o" "p" "q" "r" "s" "t" "u" "v" "w" "x" "y" "z"

LETTERS

 [1] "A" "B" "C" "D" "E" "F" "G" "H" "I" "J" "K" "L" "M" "N" "O" "P" "Q" "R" "S" "T" "U" "V" "W" "X" "Y" "Z"

mixedLetters <- c( sample( letters, 5 ), sample( LETTERS, 5 ) )
mixedLetters

 [1] "b" "u" "k" "q" "g" "Y" "X" "V" "F" "T"

Are all/any elements TRUE

Input: logical vector
Output: single logical value
Task: try, understand what happens when you use manyNumbersWithNA instead of manyNumbers.

all( manyNumbers <= 1000 )

[1] TRUE

all( manyNumbers <= 500 )

[1] FALSE

any( manyNumbers > 1000 )

[1] FALSE

any( manyNumbers > 500 )

[1] TRUE

all( !is.na( manyNumbers ) )

[1] TRUE

any( is.na( manyNumbers ) )

[1] FALSE

Which elements are TRUE

Input: logical vector Output: vector of numbers (positions)

which( manyNumbers > 900 )

[1] 7 9

which( manyNumbersWithNA > 900 )

[1]  3 15

which( is.na( manyNumbersWithNA ) )

[1]  2 10 17

Filtering vector elements

Input: any vector and filtering condition
Output: elements of the input vector
Note: several ways to get the same effect

manyNumbers[ manyNumbers > 900 ] # indexing by logical vector

[1] 1000  989

manyNumbers[ which( manyNumbers > 900 ) ] # indexing by positions

[1] 1000  989

somePositions <- which( manyNumbers > 900 )
manyNumbers[ somePositions ]

[1] 1000  989

Are some elements among other elements

Input: two vectors
Output: a logical vector corresponding to the first input vector

"A" %in% LETTERS

[1] TRUE

c( "X", "Y", "Z" ) %in% LETTERS

[1] TRUE TRUE TRUE

all( c( "X", "Y", "Z" ) %in% LETTERS )

[1] TRUE

all( mixedLetters %in% LETTERS )

[1] FALSE

any( mixedLetters %in% LETTERS )

[1] TRUE

mixedLetters[ mixedLetters %in% LETTERS ]

[1] "Y" "X" "V" "F" "T"

mixedLetters[ !( mixedLetters %in% LETTERS ) ]

[1] "b" "u" "k" "q" "g"

manyNumbers %in% 300:600

 [1] FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE  TRUE  TRUE FALSE  TRUE
[18] FALSE FALSE  TRUE

which( manyNumbers %in% 300:600 )

[1] 14 15 17 20

sum( manyNumbers %in% 300:600 )

[1] 4

Pick one of two (three) depending on condition

Input: a logical vector and two vectors additional vectors (for TRUE, for FALSE)
Output: elements of the additional vectors
Note: it can take care of NAs

if_else( manyNumbersWithNA >= 500, "large", "small" )

 [1] "small" NA      "large" "large" "small" "large" "small" "small" "large" NA      "large" "small" "large"
[14] "large" "large" "large" NA      "small" "small" "large" "large" "large" "small"

if_else( manyNumbersWithNA >= 500, "large", "small", "UNKNOWN" )

 [1] "small"   "UNKNOWN" "large"   "large"   "small"   "large"   "small"   "small"   "large"   "UNKNOWN"
[11] "large"   "small"   "large"   "large"   "large"   "large"   "UNKNOWN" "small"   "small"   "large"  
[21] "large"   "large"   "small"

# here integer 0L is needed instead of real 0.0 
# manyNumbersWithNA contains integer numbers and the method complains
if_else( manyNumbersWithNA >= 500, manyNumbersWithNA, 0L )

 [1]    0   NA  989  677    0  818    0    0  724   NA  502    0  859  686 1000  846   NA    0    0  877
[21]  618  892    0

Duplicates and unique elements

Input: a vector

unique( duplicatedNumbers )

[1] 4 5 1 3 2

unique( c( NA, duplicatedNumbers, NA ) )

[1] NA  4  5  1  3  2

duplicated( duplicatedNumbers )

 [1] FALSE  TRUE FALSE FALSE FALSE FALSE  TRUE  TRUE  TRUE  TRUE

Positions of max/min elements

which.max( manyNumbersWithNA )

[1] 15

manyNumbersWithNA[ which.max( manyNumbersWithNA ) ]

[1] 1000

which.min( manyNumbersWithNA )

[1] 19

manyNumbersWithNA[ which.min( manyNumbersWithNA ) ]

[1] 52

range( manyNumbersWithNA, na.rm = TRUE )

[1]   52 1000

Sorting/ordering of vectors

manyNumbersWithNA

 [1]  193   NA  989  677  105  818  133  302  724   NA  502  416  859  686 1000  846   NA   69   52  877
[21]  618  892  393

sort( manyNumbersWithNA )

 [1]   52   69  105  133  193  302  393  416  502  618  677  686  724  818  846  859  877  892  989 1000

sort( manyNumbersWithNA, na.last = TRUE )

 [1]   52   69  105  133  193  302  393  416  502  618  677  686  724  818  846  859  877  892  989 1000
[21]   NA   NA   NA

sort( manyNumbersWithNA, na.last = TRUE, decreasing = TRUE )

 [1] 1000  989  892  877  859  846  818  724  686  677  618  502  416  393  302  193  133  105   69   52
[21]   NA   NA   NA

manyNumbersWithNA[1:5]

[1] 193  NA 989 677 105

order( manyNumbersWithNA[1:5] )

[1] 5 1 4 3 2

rank( manyNumbersWithNA[1:5] )

[1] 2 5 4 3 1

sort( mixedLetters )

 [1] "b" "F" "g" "k" "q" "T" "u" "V" "X" "Y"

Ranking of vectors

manyDuplicates <- sample( 10:15, 10, replace = TRUE )
rank( manyDuplicates )

 [1] 4.5 7.0 9.0 9.0 6.0 9.0 2.5 1.0 2.5 4.5

rank( manyDuplicates, ties.method = "min" )

 [1] 4 7 8 8 6 8 2 1 2 4

rank( manyDuplicates, ties.method = "random" )

 [1]  5  7  9  8  6 10  3  1  2  4

Rounding numbers

v <- c( -1, -0.5, 0, 0.5, 1, rnorm( 10 ) )
v

 [1] -1.00000000 -0.50000000  0.00000000  0.50000000  1.00000000  0.42724316  1.04802313  1.18628196
 [9]  1.48550990 -0.82549720 -0.15091809 -0.04325337 -0.32946535 -0.83780534 -0.14797026

round( v, 0 )

 [1] -1  0  0  0  1  0  1  1  1 -1  0  0  0 -1  0

round( v, 1 )

 [1] -1.0 -0.5  0.0  0.5  1.0  0.4  1.0  1.2  1.5 -0.8 -0.2  0.0 -0.3 -0.8 -0.1

round( v, 2 )

 [1] -1.00 -0.50  0.00  0.50  1.00  0.43  1.05  1.19  1.49 -0.83 -0.15 -0.04 -0.33 -0.84 -0.15

floor( v )

 [1] -1 -1  0  0  1  0  1  1  1 -1 -1 -1 -1 -1 -1

ceiling( v )

 [1] -1  0  0  1  1  1  2  2  2  0  0  0  0  0  0

Naming vector elements

heights <- c( Amy = 166, Eve = 170, Bob = 177 )
heights

Amy Eve Bob 
166 170 177

names( heights )

[1] "Amy" "Eve" "Bob"

names( heights ) <- c( "AMY", "EVE", "BOB" )
heights

AMY EVE BOB 
166 170 177

heights[[ "EVE" ]]

[1] 170

Generating grids

expand_grid( x = c( 1:3, NA ), y = c( "a", "b" ) )

# A tibble: 8 x 2
      x y    
  <int> <chr>
1     1 a    
2     1 b    
3     2 a    
4     2 b    
5     3 a    
6     3 b    
7    NA a    
8    NA b

Generating combinations

combn( c( "a", "b", "c", "d", "e" ), m = 2, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  "c"  "d"  
[2,] "b"  "c"  "d"  "e"  "c"  "d"  "e"  "d"  "e"  "e"

combn( c( "a", "b", "c", "d", "e" ), m = 3, simplify = TRUE )

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "a"  "a"  "a"  "a"  "a"  "a"  "b"  "b"  "b"  "c"  
[2,] "b"  "b"  "b"  "c"  "c"  "d"  "c"  "c"  "d"  "d"  
[3,] "c"  "d"  "e"  "d"  "e"  "e"  "d"  "e"  "e"  "e"

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